Open data
Pl@ntNet data on GBIF
Section titled “Pl@ntNet data on GBIF”The most reliable Pl@ntNet observations are now integrated into the international GBIF (Global Biodiversity Information Facility) database. GBIF is a global network and research infrastructure funded by the world’s governments, providing free and open access to biodiversity data. The publication of Pl@ntNet data on this platform allows researchers worldwide to use them for studies in ecology, agronomy, or biodiversity conservation.
Pl@ntNet data contribute to research on various themes, such as the impact of climate change on plant species distribution, the spread of invasive alien species, and interactions between plants and pollinators.
Only observations considered most reliable in terms of species identification are shared on GBIF. This includes two main categories:
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Observations shared and validated by the community (accessible here).
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Identification requests for which the automatic recognition algorithm reaches a sufficiently high confidence level (accessible here).
Observations validated by the community are published with their images under a Creative Commons CC-BY-SA license, mentioning the author’s name. In contrast, for identification requests, only the location and the plant name are shared, without any additional personal information.
This exceptional contribution to science is the result of the commitment of all Pl@ntNet users, whether they are casual users, passionate contributors, or data curators.
GBIF API
Section titled “GBIF API”It is also possible to use the GBIF API to explore and download Pl@ntNet data.
Pl@ntNet identification API
Section titled “Pl@ntNet identification API”The Pl@ntNet identification service is also available as an API, see here.
Pl@ntNet-300K image dataset
Section titled “Pl@ntNet-300K image dataset”Camille Garcin, Alexis Joly, Pierre Bonnet, Jean-Christophe Lombardo, Antoine Affouard, Mathias Chouet, Maximilien Servajean, Joseph Salmon, Titouan Lorieul. Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution. NeurIPS 2021 - 35th Conference on Neural Information Processing Systems, December 2021, Virtual Conference, France.